Astronomy and astrophysics data sets have been increasing over the last decade as many new telescopes and detectors have been launched. High-redshift radio galaxies are powerful radio sources that are the ideal targets to discuss the evolution of Hi; thus, they are one of the key points to understand the universe evolution and formation. The cloud computing systems are applicable with IoT considering the processing time over training data using ANN. Machine learning is a subfield of AI and it is used by scientists for prediction or classification purposes considering the input data. Machine learning algorithms have become increasingly popular among astronomers and are now used for a wide range of astrophysical calculations and fields. This paper proposes five types of machine learning algorithms, namely back-propagation neural networks (BPNN), decision tree algorithm (DT), gradient boosting classifier algorithm (GBA), radial basis function neural network (RBFNN), and support vector machine (SVM). The machine learning models are implemented to classify and compare the results of high-redshift radio galaxies by their location in the sky in ELAIS-N1, ELAIS-N2, the Lockman hole, VIMOS fields, in order to increase the performance efficiency, accuracy and improve our confidence considering the critical nature of the calculations in redshift galaxies. When 100 instances were considered, back-propagation neural networks achieved an accuracy rate of 70%; however, when 200 instances were considered, radial basis function neural networks achieved an accuracy rate of 88.2%.

Accurate and early diagnosis of COVID-19 can reduce the mortality rate caused by the disease across the globe. Computer-aided diagnosis (CAD) helps radiologists efficiently extract and diagnose the abnormal portions. The healthcare market is currently experiencing rapid development owing to the Internet of Things (IoT). This paper proposes a framework that integrates machine learning and intelligence-based e-Health service systems that can be used as an application of the Internet of Medical Things (IoMT) for the early diagnosis of COVID-19 disease. This framework consists of a classification approach for diagnosing the abnormalities in lung CT images using a whale optimisation algorithm (WOA) optimised wavelet neural network (WNN). WOA optimises the input features, initial weights, hidden nodes, momentum constant, and learning parameters of a WNN in the proposed system. The proposed approach extracts the Laws 16 Texture Energy Measures (LTEM) from the preprocessed CT lung images and classifies the abnormal regions with the help of a WNN classifier. The proposed framework is evaluated using a publicly available COVID-19 dataset that contains both theCOVID-19 and non-COVID-19 cases. The result shows that theproposed approach has a sensitivity of 82%, a specificity of 73.3%, and an accuracy of 84.8%.

This article focuses on a new electroencephalogram (EEG)-based system for the early detection of epileptic episodes that is made possible by the Internet of Things (IoT). The system is made up of two important units, namely, the multichannel EEG recording unit and the seizure detection unit. Since epileptic information is more useful when included in multichannel EEG data from many brain regions, the primary goals of this work are: designing and developing the seizure detection unit, making use of spike-statistical (SS) flower pollination algorithm (FPA)-based critical spectral verge (CSV)-derived features termed as SS-CSV; and employing the convolutional neural network (CNN) method in an IoT-enabled EEG monitoring system to detect EEG seizures. The presented system performed better, with an average accuracy of 98.48% with the CNN classifier. Neuroexperts will find this approach very useful to analyze seizure information, especially in wearable medical devices.

Abstract Internet of Things (IoT) and cyber‐physical systems demand proper real‐time data processing and analysis to fulfill the essential characteristics of seamless computing services such as less processing time, dynamic network management, and location awareness. With the evolution of mobile edge computing, numerous security and privacy issues have been addressed including policy management, authentication, access control, and confidentiality. The technologies such as big‐data, Internet of Things, and cloud have improved the development of modern healthcare systems to improve the quality of medical services. It is nowadays integrating the computing methods and communication technologies such as wireless sensor networks, radio frequency identification, smartphones, and location awareness to collect and analyze the physiological data of the patient. The e‐Health entities such as medical device, client, and server rely on analytical models to carry out early diagnosis and remote monitoring. As it is based on e‐Health applications, a real‐time healthcare system is preferred to integrate with mobile cloud computing. Since the medical data are hugely exposed to vulnerabilities, a lightweight privacy‐aware secure authentication (LPASA) scheme is presented. To achieve better secure efficiencies, this article is preferable to use lightweight cryptographic operations, namely, one‐way hashing function and bitwise exclusive‐OR. Moreover, the formal and informal security analysis shows that the proposed LPASA can withstand the possible active and passive attacks such as replay, man‐in‐the‐middle, privileged‐insider, online‐guessing, and so forth. The experimental analysis demonstrates that the proposed LPASA scheme achieves a better quality of services in terms of packet delivery ratio, energy consumption per data delivery bit, and percentage of packets with low latency to adopt in resource‐constrained environments.

Abstract Osteoarthritis and rheumatoid are most common form of arthritis disorder, affecting millions of people worldwide. This article presents a computer aided detection system (CAD) for early knee osteoarthritis and rheumatoid detection using X‐ray images and machine learning classifiers. This work also proposed a novel feature extractor from X‐ray images of knee to assist in detection and classification, called explainable Renyi entropic segmentation with Internet of Things (IoT) framework. The proposed method later utilizes model agnostic algorithm using post hoc explainability for extracting relevant information from prediction of knee joint segmentation. CAD system is integrated with an IoT framework and can be used remotely to assist medical practitioners in treatments of knee arthritis. The presented results show commendable improvement over different existing feature extractors in combination with different classifiers. The best result of proposed extractor method was obtained when combined with random forest classifier having Euclidean hyperparameter that gave an accuracy of 95.23%, among all the evaluators. The obtained results show the effectiveness of proposed feature extractor model to determine relevant features from knee and describe the suitable knee disorders.

The major issues and challenges of the Industrial Internet of Things (IIoT) include network resource management, self-organization; routing, mobility, scalability, security, and data aggregation. Resource management in IIoT is a challenging issue, starting from the deployment and design of sensor nodes, networking at cross-layer, networking software development, application types, environmental conditions, monitoring user decisions, querying process, etc. In this article, computational intelligence (CI) and its computing, such as neural networks and fuzzy logic, are used to tackle the challenges of resource management in the IIoT. The incorporation of the neuro-fuzzy technique into the IIoT contributes to the self-managing intelligence systems’ self-organizing and self-sustaining capabilities, offering real-time computations and services in a pervasive networking environment. Most of the problems in IIoT are real-time based; they require fast computation, real-time optimal solutions, and the need to be adaptive to the situation of the events and data traffic to achieve the desired goals. Hence, neural networks and fuzzy sets would form appropriate candidates for implementing most of the computations involved in the issues of resource management in IIoT networks. A real-time testbed network is simulated and implemented on the Crossbow mote (sensor node) using TinyOS.

Coronaviridae family consists of many virulent viruses with zoonotic properties that can be transmitted from animals to humans. Different strains of these viruses have caused pandemic in the past such as Severe Respiratory Syndrome Coronavirus (SARS-CoV) in 2002, Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 and recently Severe Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) also known as COVID-19 in December 2019. Scientists utilised different approaches for the detection and characterisation of CoVs using samples such as serum, throat swabs, nose swabs, nasopharyngeal aspirates and bronchoalveolar lavages. The two common approaches include antigen-based approach and molecular diagnostic approach, which are hindered by limitations such as low sensitivity and requirement for high level of biosafety during isolation of the virus from cell culture. Thus, there is a need for developing a more rapid, sensitive, simple and cheap diagnostic kit for diagnosis of different strains of coronavirus. In this article, we overview 2019 novel coronavirus, pandemic, prior epidemics, diagnosis, treatments, identification of drugs detection based on classification and prediction using artificial intelligence-driven tools. We also overview in-lab molecular testing and on-site testing using CRISPR-based biosensing tools. We also outline limitations of laboratory techniques and open-research issues in the current state of CRISPR-based biosensing applications and artificial intelligence for treatment of Coronaviruses.

Analyzing real-time news feeds and their impacts in the real world is a complex task in the social networking arena. Particularly, countries with a multilingual environment have various patterns and perceptions of news reports considering the diversity of the people. Multilingual and multimodal news analysis is an emerging trend for evaluating news source neutralities. Therefore, in this work, four new deep news particle filtering techniques were developed, including generic news analysis, sequential importance re-sampling (SIR) -based news particle filtering analysis, reinforcement learning (RL) -based multimodal news analysis, and deep Convolution neural network (DCNN) -based multi-news filtering approach, for news classification. Results indicate that these techniques, which primarily employ particle filtering with multilevel sampling strategies, produce 15% to 20% better performance than conventional news analysis techniques.

Corona virus disease 19 (COVID-19) is an infectious disease which is having a significant health and economic impact across the world. The primary source for the transmission of the disease, its detection and treatment methods are still unknown. Hence, a scientific response to this new corona virus is being hampered by a lack of knowledge on how it spreads, possible prevention measures and vaccinations, which all need to be investigated further. Artificial intelligence (AI) and computer communication networks have a role to play, especially machine learning (ML) due to its learning-from samples capability and applicability over distributed computer systems and networks. This special issue features eight selected papers with high quality. The article, ‘Prediction of COVID-19 active cases using exponential and non-linear growth models,’ compares different AI models against a newly proposed one (Mahanty et al., 2022). The main objective of this paper was discovering the rate of infection spread in India, Pakistan, Myanmar, Brazil, Italy, and Germany, in addition to designing a susceptible-infectious-recovered (SIR), Verhulst, Gompertz, and proposed model for the assessment of the disease spread. And finally, providing a prediction method for the COVID-19 outbreak using all the said models. The article titled ‘Value of medical imaging artificial intelligence in the diagnosis and treatment of new coronavirus pneumonia’ applies AI to medical imaging, combined with embedded technology, RFID technology and signal processing technology, and applies the new coronavirus pneumonia image to the AI environment after processing, assisting doctors in diagnosis of the disease, and providing relevant information about patients record and manage the diagnosis and save and accumulate the experience and knowledge of famous doctors through the expert system, and then perform corresponding operations and analysis (Jia et al., 2022). Through the medical image intelligent analysis system, the risk of medical imaging AI diagnosis is reduced from 81% to 11%, which greatly reduces the hidden safety hazards for doctors and patients, reduces the workload of doctors, and also reduces the cost of medical care by 79%. In the article with the title ‘Endoscopic image recognition method of gastric cancer based on deep learning model’, Qiu et al. (2022) aim to improve the efficiency of gastric cancer (GC) diagnosis. So deep learning (DL) algorithms are tentatively used to assist doctors in the diagnosis of gastric cancer. In the experiment, the collected 3591 gastroscopic images were divided into network training set and experimental verification test set. The lesion samples in the image are all marked by many endoscopists with many years of clinical experience. In order to improve the experimental effect, 5261 endoscopic images were obtained by expanding the training set. Then the obtained training set is fed into the convolutional neural network (CNN) for training, and finally get the algorithm model DLU-Net. Authors concluded that the DL algorithm model constructed in this paper can effectively identify the staging characteristics of cancer and other similar diseases as well as the gastroscopic images, greatly improve efficiency, and effectively assist physicians in the diagnosis of GC under gastroscopy. The article titled ‘Fuzzy logic control theory in clinical anesthesia’, mainly studies the application of fuzzy logic control theory in clinical anaesthesia (Tian et al., 2022). First, after introducing the basic content of fuzzy logic control theory, the determination method of commonly used membership functions, the relevant knowledge of clinical anaesthesia, and the fuzzy logic code rate control model, this article describes in detail the basic principle diagram of the clinical anaesthesia control system and the clinical anaesthesia process. The mathematical model of clinical anaesthesia control system is constructed based on the data fusion technology of the parameters of anaesthesia depth monitoring. The parameters in the model are adjusted by the time domain analysis method to measure the dynamic characteristics, and the stability of the system is analysed by root locus method. The heart rate does not change significantly when it is lower than 1MAC, and the heart rate increases when it reaches 1.5 ~ 2MAC. Experimental results show that the application of fuzzy logic control theory to clinical anaesthesia can reduce the risk of clinical anaesthesia. By means of the CNN, the article ‘Medical image analysis of multiple myeloma based on convolutional neural network’, provides the application of neural network algorithm in multiple myeloma (He & Zhang, 2022). As such, the CNN model is constructed using existing medical data, and the retained case image data are input into the constructed CNN to verify the accuracy of the neural network. The results show that the accuracy rate of the neural network model constructed in this study is 0.87, which is higher than the accuracy rate of manual detection of 0.77. It can be concluded that using magnetic resonance imaging (MRI) to classify multiple myeloma has a high accuracy rate. Therefore, it has been proved that the CNN model established in this paper is effective. The proposed results prove that the neural network algorithm can be applied to MRI analysis, which helps to improve the efficiency of multiple myeloma diagnosis not only in COVID-19 related studies, but many other medical fields as well. On the other hand, existing and utilized neural networks security are not fully considered image segmentation. Therefore, the article, titled ‘Image segmentation algorithm of lung cancer based on neural network model’, explores the application of neural network algorithm model in lung imaging, and provides a reference for the application and development of artificial neural network algorithm in lung cancer medical mirroring, while promoting the development of the artificial neural network in this field (He et al., 2022). It is hoped that the application of neural network algorithms in medical imaging can improve the survival rate and cure rate of lung diseases. In this study, an artificial neural network algorithm model was selected to establish a lung cancer recognition model. After determining the lung cancer lesion area, the image segmentation algorithm was used to separately display the lung cancer lesion area, and a comparison experiment was designed to verify the accuracy of the model. Using artificial neural networks to identify lung cancer has a shorter diagnosis time and higher accuracy. Combining image retrieval methods with lung cancer image segmentation algorithms can clearly show the lesion area of lung cancer. Therefore, the lung cancer image segmentation algorithm based on the neural network model has good recognition performance. In the future development of intelligent medical imaging technology, artificial neural networks will be trained to perform medical image recognition and diagnosis tasks, which can reduce diagnosis time and improve diagnosis efficiency. In the article, titled ‘Clinical study of serum procalcitonin in the early diagnosis of burns and sepsis under the background of healthy clouds’, the purpose is to diagnose the sepsis early while utilizing the cloud services (Huang et al., 2022). The clinical symptoms and vital signs of sepsis are not particularly abnormal, and imaging examination may cause the focus of infection to be incorrect. As a result, the positive rate of positive results is low, which seriously affects the timely diagnosis and treatment of patients. Experimental data show that serum PCT of non-septic patients is obvious during the six groups of experiments 1–5 days, 6–10 days, 11–15 days, 16–20 days, 21–25 days, 26–30 days after treatment Serum PCT levels below sepsis. The data recorded during the experiment are in accordance with the relevant principles of statistics to ensure that the experiment is true and effective. The experimental results show that the PCT of burn sepsis group is higher than that of the cured group without burns, and the serum PCT level is crucial for the diagnosis of burn sepsis. Meanwhile, recurrent neural networks (RNN) are extensively used to determine the optimal solutions to the various class recognition problems such as image processing, prediction of biomedical data and speech recognition. With the gradient problems, RNN is losing its shade which is replaced by the long short term memory (LSTM). However, the hardware implementation of the LSTM requires more challenge due to its complexity and high power consumption which makes it unsuitable for implementation in biological internet of things (BIoT) networks for the prediction of medical diseases. Several algorithms were proposed for an effective implementation of LSTM, but hand-offs between the performance and utilization still needs improvisation. The article, titled ‘P-SCADA - A novel area and energy efficient FPGA architectures for LSTM prediction of heart arrthymias in BIoT applications’, proposes the novel energy efficient and high performance architecture pipelined stochastic adaptive distributed architectures (P-SCADA) for LSTM networks (Varadharajan & Nallasamy, 2022). In this architecture, hybrid structure has been developed with the help of new distributed arithmetic stochastic computing (DSC) along with the binary circuits to advance the performance of the FPGA such as energy, area and accuracy. The proposed system has been implemented in ARTIX-7 FPGA with special purpose software has been designed and evaluated with different ECG data sets. For the different series data, area utilization is about 40%–44% and power consumption is about 20%–25% with the prediction of accuracy of 98%. Moreover, the proposed architecture has been compared with the other existing architecture such as SPARSE architectures, normal stochastic architectures in which the proposed architecture excels in terms area, power and efficiency. The guest editors are thankful to the anonymous reviewers for their effort in reviewing the manuscripts. We are also thankful to the Editor-in-Chief, for his supportive guidance during the entire process. Fadi Al-Turjman received his PhD in computer science from Queen's University, Canada, in 2011. He is the associate dean for research and the founding director of the International Research Center for AI and IoT at Near East University, Nicosia, Cyprus. Prof. Al-Turjman is the head of Artificial Intelligence Engineering Dept., and a leading authority in the areas of smart/intelligent IoT systems, wireless, and mobile networks' architectures, protocols, deployments, and performance evaluation in Artificial Intelligence of Things (AIoT). His publication history spans over 400 SCI/E publications, in addition to numerous keynotes and plenary talks at flagship venues. He has authored and edited more than 40 books about cognition, security and wireless sensor networks' deployments in smart IoT environments, which have been published by well-reputed publishers such as Taylor and Francis, Elsevier, IET, and Springer. He has received several recognitions and best papers' awards at top international conferences. He also received the prestigious Best Research Paper Award from Elsevier Computer Communications Journal for the period 2015–2018, in addition to the Top Researcher Award for 2018 at Antalya Bilim University, Turkey. Prof. Al-Turjman has led a number of international symposia and workshops in flagship communication society conferences. Currently, he serves as book series editor and the lead guest/associate editor for several top tier journals, including the IEEE Communications Surveys and Tutorials (IF 23.9) and the Elsevier Sustainable Cities and Society (IF 7.8), in addition to organizing international conferences and symposiums on the most up to date research topics in AI and IoT.

Reverse-Transcription Polymerase Chain Reaction (RT-PCR) method is currently the gold standard method for detection of viral strains in human samples, but this technique is very expensive, take time and often leads to misdiagnosis. The recent outbreak of COVID-19 has led scientists to explore other options such as the use of artificial intelligence driven tools as an alternative or a confirmatory approach for detection of viral pneumonia. In this paper, we utilized a Convolutional Neural Network (CNN) approach to detect viral pneumonia in x-ray images using a pretrained AlexNet model thereby adopting a transfer learning approach. The dataset used for the study was obtained in the form of optical Coherence Tomography and chest X-ray images made available by Kermany et al. (2018, https://doi.org/10.17632/rscbjbr9sj.3) with a total number of 5853 pneumonia (positive) and normal (negative) images. To evaluate the average efficiency of the model, the dataset was split into on 50:50, 60:40, 70:30, 80:20 and 90:10 for training and testing respectively. To evaluate the performance of the model, 10 K Cross-validation was carried out. The performance of the model using overall dataset was compared with the means of cross-validation and the currents state of arts. The classification model has shown high performance in terms of accuracy, sensitivity and specificity. 70:30 split performed better compare to other splits with accuracy of 98.73%, sensitivity of 98.59% and specificity of 99.84%.

The deadly coronavirus virus (COVID-19) was confirmed as a pandemic by the World Health Organization (WHO) in December 2019. It is important to identify suspected patients as early as possible in order to control the spread of the virus, improve the efficacy of medical treatment, and, as a result, lower the mortality rate. The adopted method of detecting COVID-19 is the reverse-transcription polymerase chain reaction (RT-PCR), the process is affected by a scarcity of RT-PCR kits as well as its complexities. Medical imaging using machine learning and deep learning has proved to be one of the most efficient methods of detecting respiratory diseases, but to train machine learning features needs to be extracted manually, and in deep learning, efficiency is affected by deep learning architecture and low data. In this study, handcrafted local binary pattern (LBP) and automatic seven deep learning models extracted features were used to train support vector machines (SVM) and K-nearest neighbour (KNN) classifiers, to improve the performance of the classifier, a concatenated LBP and deep learning feature was proposed to train the KNN and SVM, based on the performance criteria, the models VGG-19 + LBP achieved the highest accuracy of 99.4%. The SVM and KNN classifiers trained on the hybrid feature outperform the state of the art model. This shows that the proposed feature can improve the performance of the classifiers in detecting COVID-19.

Coronavirus disease (COVID-19) is a harmful disease caused by the new SARS-CoV-2 virus. COVID-19 disease comprises symptoms such as cold, cough, fever, and difficulty in breathing. COVID-19 has affected many countries and their spread in the world has put humanity at risk. Due to the increasing number of cases and their stress on administration as well as health professionals, different prediction techniques were introduced to predict the coronavirus disease existence in patients. However, the accuracy was not improved, and time consumption was not minimized during the disease prediction. To address these problems, least square regressive Gaussian neuro-fuzzy multi-layered data classification (LSRGNFM-LDC) technique is introduced in this article. LSRGNFM-LDC technique performs efficient COVID prediction with better accuracy and lesser time consumption through feature selection and classification. The preprocessing is used to eliminate the unwanted data in input features. Preprocessing is applied to reduce the time complexity. Next, Deming Least Square Regressive Feature Selection process is carried out for selecting the most relevant features through identifying the line of best fit. After the feature selection process, Gaussian neuro-fuzzy classifier in LSRGNFM-LDC technique performs the data classification process with help of fuzzy if-then rules for performing prediction process. Finally, the fuzzy if-then rule classifies the patient data as lower risk level, medium risk level and higher risk level with higher accuracy and lesser time consumption. Experimental evaluation is performed by Novel Corona Virus 2019 Dataset using different metrics like prediction accuracy, prediction time, and error rate. The result shows that LSRGNFM-LDC technique improves the accuracy and minimizes the time consumption as well as error rate than existing works during COVID prediction.

Pandemic novel Coronavirus (Covid-19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid-19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid-19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures. Due to the lack of accurate Covid-19 records and uncertainty, the standard techniques are being failed to correctly predict the epidemic global effects. To address this issue, we present an Artificial Intelligence (AI)-based meta-analysis to predict the trend of epidemic Covid-19 over the world. The powerful machine learning algorithms namely Naïve Bayes, Support Vector Machine (SVM) and Linear Regression were applied on real time-series dataset, which holds the global record of confirmed, recovered, deaths and active cases of Covid-19 outbreak. Statistical analysis has also been conducted to present various facts regarding Covid-19 observed symptoms, a list of Top-20 Coronavirus affected countries and a number of coactive cases over the world. Among the three machine learning techniques investigated, Naïve Bayes produced promising results to predict Covid-19 future trends with less Mean Absolute Error (MAE) and Mean Squared Error (MSE). The less value of MAE and MSE strongly represent the effectiveness of the Naïve Bayes regression technique. Although, the global footprint of this pandemic is still uncertain. This study demonstrates the various trends and future growth of the global pandemic for a proactive response from the citizens and governments of countries. This paper sets the initial benchmark to demonstrate the capability of machine learning for outbreak prediction.

The rapid development forms a new transition of information technologies to offer an intelligent manufacturing. The manufacturer has revolutionized the stages of product lifecycle including process planning and maintenance for the early detection of potential system failures and proactive management. Technological advancements including big data, the cloud, and the Internet of Things have applied digital-twin for industrial practice. It has low-power wireless-enabled devices to play a vital role in various industrial automation systems such as industry logistics, portable equipment, and intelligent wireless monitoring. It is evident that industrial manufacturers are nowadays aiming to transform the machine into fully automated systems that not only control the operation of the equipment but also try to meet the demand of future markets effectively. One of the challenging issues in the automation of the machinery process is the deployment of reliable systems to analyze the machinery condition such as fault diagnosis. Thus, this article proposes a digital-twin-assisted fault diagnosis using deep transfer learning to analyze the operational conditions of machining tools. Moreover, this proposed system has developed an intelligent tool-holder that integrates a k-type thermocouple and cloud data acquisition system over the WiFi module. The analytical study proves that this intelligent tool-holder provides better accuracy to demonstrate the optimization of milling and drilling operations of cutting tools.

Internet-of-Things (IoT) enabled cyber-physical systems (CPS) is a system in which communication between the physical devices and the cyber environment runs independently without any user interaction. Several optimization algorithms have been used for determining the optimal solutions that can reduce the production cost and/or enhance the production efficiency with in limited time-periods. However, existing optimization approaches have failed to solve the issues in the complex manufacturing process. To overcome this issue, a novel technique called directed acyclic graph theory based multiobjective oppositional learnt artificial ant colony resource optimization (DAGT-MOLAACRO) technique has been introduced in this study for solving the complex manufacturing process in the industry. Initially, IoT devices are used in the industrial sector for sensing and collecting data. Then the collected data is sent to the cyberspace of the CPS system with the least latency. Then, the CPS system collects the data generated from the industrial IoT devices that is stored in cyberspace with lesser memory consumption. MOLAACRO is applied to find the optimal solution among the population that satisfies the resource constraints by constructing the directed acyclic graph. In this way, the DAGT-MOLAACRO technique reduces the time complexity with minimal latency and computation overhead. For verification purposes, our experimental work has been carried out using different performance metrics such as data latency, time complexity, and computation overhead with respect to the number of IoT devices and the amount of data collected. The results show that the DAGT-MOLAACRO technique has better performance with reductions in terms of time complexity by 10%, latency by 17%, and the computation overhead by 11% against the existing works in literature.

Internet of Things (IoT) is a new technology with multiple smart connected sensors capable of processing, storing, and computing. Industrial IoT (IIoT) is used in industrial applications, such as infrastructure, medical, logistics, and energy efficiency in smart grids. The network lifetime will be extended when sensor node energy usage is effectively controlled. This article proposed a multilayer threshold cluster-based energy-efficient low power and lossy networks (MTCEE-LLN) protocol for IIoT devices to decrease the network data traffic, sensor node energy consumption (EC) and also extends the network lifetime. The proposed scheme works in three phases: 1) network creation; 2) intra clustering; and 3) intercluster routing. The MTCEE-LLN forms equal-sized cluster in each transmission and elects the cluster head (CH). It maintains the destination-oriented directed acyclic graph (DODAG) to performs data transmission from the downward layer to the${\mathrm{ DODAG}}_{\mathrm{ root}}$. Furthermore, it aggregates the data packets in the cluster node to increases the network lifetime by reducing the number of redundant data packet transmissions. The proposed routing protocol has been evaluated based on different performance parameters such as packet loss rate (PLR), EC, control packet rate (CPR), and Node Failure Ratio. The simulated result proves its effectiveness compared to other traditional routing protocols.

Artificial intelligence based Internet of Things enables autonomous communication among social networks and IoT to leverage the promising solution in the modern paradigms. It can provide an interactive platform across the globe to enrich the quality of networking services to the end users. Of late, the expansion of information-centric networking has brought an incredible technique, known as public auditing scheme, for IoT-enabled sensor technologies. It uses a cloud-based medical cyber-physical system (M-CPS) to rely on cloud computing that ensures fast computing and reliable data storage. Since the medical file is so vital to involve precise diagnoses, data integrity and verification have lately become the data auditing tool. To exploit proxy authorizer and trusted auditor, an identity-based proxy authorized outsourcing with public auditing (ID-PAOPA) is proposed. It uses proxy authorization and verification to upload medical data over cloud-based M-CPS. To substantiate the finding, this article provides security proof based on the EC-DLP assumption. Finally, the performance analysis proves that the proposed ID-PAOPA achieves less computation and auditing timing to fulfill the objectives of cloud-based M-CPS.

The rapid advancements in smart grid (SG) technology extend a large number of applications including vehicle charging, smart buildings, and smart cities through the efficient use of advanced communication architecture. However, the underlying public channel leads these services to be vulnerable to many threats. Recently, some security schemes were proposed to counter these threats. However, the insecurities of some of these schemes against key compromise impersonation (KCI) and related attacks or compromise on efficiency calls for a secure and efficient authentication scheme for SG infrastructure. A new scheme to secure SG communication is presented in this article to provide a direct device-to-device authentication among smart meter and neighborhood area network gateway. Designed specifically to resists KCI and related attacks, the proposed scheme is more secure and completes the authentication procedure by using the least communication cost as compared with related schemes, which is evident through security and efficiency comparisons.

The recent developments in the wireless body area networks (WBAN) play a vital role in the modern remote health care monitoring system. In WBAN, the deployed intelligent, resource-limited body sensors will collect the patient's biological information (BI) and communicate the same to the doctor for further action through the Internet. But during the communication among the WBAN entities, the privacy, security of the BI, and user's personal data need to be protected against various security threats. To address these security flaws, in this article, a computationally efficient privacy-preserving anonymous authentication scheme is proposed for resource-limited WBAN. Also, it preserves the physical security of sensors and the confidentiality of BI and provides conditional privacy to the WBAN users. The comprehensive security and performance evaluation phase ensures that the proposed scheme is efficient and secure in terms of computational and communication complexity when compared with the other conventional schemes.

Over the years cloud computing has seen significant evolution in terms of improvement in infrastructure and resource provisioning. However the continuous emergence of new applications such as the Internet of Things (IoTs) with thousands of users put a significant load on cloud infrastructure. Load balancing of resource allocation in cloud-oriented IoT is a critical factor that has a significant impact on the smooth operation of cloud services and customer satisfaction. Several load balancing strategies for cloud environment have been proposed in the past. However the existing approaches mostly consider only a few parameters and ignore many critical factors having a pivotal role in load balancing leading to less optimized resource allocation. Load balancing is a challenging problem and therefore the research community has recently focused towards employing machine learning-based metaheuristic approaches for load balancing in the cloud. In this paper we propose a metaheuristics-based scheme Data Format Classification using Support Vector Machine (DFC-SVM), to deal with the load balancing problem. The proposed scheme aims to reduce the online load balancing complexity by offline-based pre-classification of raw-data from diverse sources (such as IoT) into different formats e.g. text images media etc. SVM is utilized to classify “n” types of data formats featuring audio video text digital images and maps etc. A one-to-many classification approach has been developed so that data formats from the cloud are initially classified into their respective classes and assigned to virtual machines through the proposed modified version of Particle Swarm Optimization (PSO) which schedules the data of a particular class efficiently. The experimental results compared with the baselines have shown a significant improvement in the performance of the proposed approach. Overall an average of 94% classification accuracy is achieved along with 11.82% less energy 16% less response time and 16.08% fewer SLA violations are observed.

Mobile network is a collection of devices with dynamic behavior where devices keep moving, which may lead to the network track to be connected or disconnected. This type of network is called Intermittently Connected Mobile Network (ICMN) . The ICMN network is designed by splitting the region into `n' regions, ensuring it is a disconnected network. This network holds the same topological structure with mobile devices in it. This type of network routing is a challenging task. Though research keeps deriving techniques to achieve efficient routing in ICMN such as Epidemic, Flooding, Spray, copy case, Probabilistic, and Wait, these derived techniques for routing in ICMN are wise with higher packet delivery ratio, minimum latency, lesser overhead, and so on. A new routing schedule has been enacted comprising three optimization techniques such as Privacy-Preserving Ant Routing Protocol (PPARP), Privacy-Preserving Routing Protocol (PPRP), and Privacy-Preserving Bee Routing Protocol (PPBRP) . In this paper, the enacted technique gives an optimal result following various network characteristics. Algorithms embedded with productive routing provide maximum security. Results are pointed out by analysis taken from spreading false devices into the network and its effectiveness at worst case. This paper also aids with the comparative results of enacted algorithms for secure routing in ICMN.

Software piracy is an act of illegal stealing and distributing commercial software either for revenue or identify theft. Pirated applications on Android app stores are harming developers and their users by clone scammers. The scammers usually generate pirated versions of the same applications and publish them in different open-source app stores. There is no centralized system between these app stores to prevent scammers from publishing pirated applications. As most of the app stores are hosted on cloud storage, therefore a cloud-based interaction system can prevent scammers from publishing pirated applications. In this paper, we proposed IoT-based cloud architecture for clone detection using program dependency analysis. First, the newly submitted APK and possible original files are selected from app stores. The APK Extractor and JDEX decompiler extract APK and DEX files for Java source code analysis. The dependency graphs of Java files are generated to extract a set of weighted features. The Stacked-Long Short-Term Memory (S-LSTM) deep learning model is designed to predict possible clones. Experimental results have shown that the proposed approach can achieve an average accuracy of 95.48% among clones from different application stores.

Summary As the need of energy is increasing worldwide, the energy availability globally is quite less than the demands for it. Thus, the importance to consider other means of electricity generation is looked into for Sub‐Saharan Africa, which is enormously rich in solar radiant. With just an average of 5.5 kWh/m 2 , countries in the region can generate over 2000 TWh of electricity daily by just covering 1% of its total land area with solar panels. To meet up with the citizen's energy demands, steps have to be initiated to reduce the continuous dependence on fossil fuel. This can be done by integrating renewable energy sources into the grid. It of course requires urgent need to upgrade the existing grid systems into a Smart Grid. The advantages for this smart grid upgrade and the development in storage devices have been highlighted in this article, including its possible optimization and metering technologies.

Real-estate advertisements through electronic and print media are bringing considerable fortune to the global real-estate sector. However, innovative advertisement methods must be adopted if real estate aims to transform into smart real estate. The current study, which is based on a systematic literature review of 58 articles published in the last decade, identifies key media for real-estate advertisements as print media (e.g., magazines, brochures, newspapers, and digests), electronic media (e.g., websites, social media, and other digital tools), and mixed methods (e.g., billboards, signs and banners, and personalized messaging). This study takes the case of Kingsford suburb in the eastern Sydney area, and investigates the performance of the Australian real-estate industry in general and lists the key dynamics of properties in Kingsford and its prominent real-estate agencies. An unmanned aerial vehicle (UAV)-based smart real-estate advertisement material delivery system is proposed to deliver advertisement materials and gifts to the potential customers of these agencies. The system paths are optimized through four Java-run algorithms: greedy, interroute, intraroute, and Tabu. Results based on six cases, three each for rent and sales with varying numbers of customers and UAVs and an 8-h operating time, indicate that the Tabu algorithm provides the best-optimized paths in all cases, followed by the interroute, intraroute, and greedy algorithms. However, the inter- and intraroute algorithms show superior performance in terms of computation speed. The proposed framework is a practical approach in disrupting the real-estate advertising sector, thereby helping this sector transform into a smart real estate consistent with industry 4.0 goals.

Deep learning artificial intelligence (AI) is a booming area in the research field. It allows the development of end-to-end models to predict outcomes based on input data without the need for manual extraction of features. This paper aims for evaluating the automatic crack detection process that is used in identifying the cracks in building structures such as bridges, foundations or other large structures using images. A hybrid approach involving image processing and deep learning algorithms is proposed to detect automatic cracks in structures. As cracks are detected in the images they are segmented using a segmentation process. The proposed deep learning models include a hybrid architecture combining Mask R-CNN with single layer CNN, 3-layer CNN, and8-layer CNN. These models utilizes depth wise convolution with varying dilation rates for efficiently extracting diversified features from the crack images. Further, performance evaluation shows that Mask R-CNN with a single layer CNN achieves an accuracy of 97.5% on a normal dataset and 97.8% on a segmented dataset. The Mask R-CNN with 2-layer convolution resulted in an accuracy of 98.32% on a normal dataset and 98.39% on a segmented dataset. The Mask R-CNN with 8-layers convolution achieves an accuracy of 98.4% on a normal dataset and 98.75% on a segmented dataset. The proposed Mask R-CNN have proved its feasibility in detecting cracks in huge building and structures.

Technological development expands the computation process of smart devices that adopt the telecare medical information system (TMIS) to fulfill the demands of the healthcare organization. It provides better medical identification to claim the features namely trustworthy, efficient, and resourceful. Moreover, the telecare services automate the remote healthcare monitoring process to ease professional workloads. Importantly, it is conceived to be more timesaving, economical, and easy healthcare access. Cloud-Based Medical Healthcare (CBMH) system is a standard platform that gives its support to the patients for emergency treatment from the medical experts over Internet communication. Since the medical records are very sensitive, security protection is much necessitated. In addition, patient anonymity should be well preserved. In 2016, Chiou et al. proposed a mutual authentication protocol for the Telecare Medical Information System (TMIS) using Cloud Environment (CE). They claim that their protocol satisfies patient anonymity. However, this paper proves that the Chiou et al. scheme is not only completely insecure against the patient anonymity, health-report revelation, health-report forgery, report confidentiality, and non-repudiation but also fails to validate the service access against verifiability, undeniability and unforgeability. In order to provide better mutual authenticity, this paper suggests the framework of smart service authentication to cross-examine the common secret session key among the communication entities. In order to examine the security properties, formal and informal verification was carried out. Lastly, to prove the security and performance efficiency of a system, the proposed SSA framework was implemented using FPGA and Moteiv TMote Sky-Mote. A proposed smart service authentication (SSA) framework is presented to ensure better data security between the patients and the physicians. The formal and informal security analysis proves the significance of the SSA framework model to withstand the security attacks such as health-report forgery, health-report revelation, server-spoofing etc. As a result, it is claimed that it can be well suited for TMIS.

Summary This article is aimed at designing positioning system using visible light communication technology by incorporating three different diversity schemes, namely, transmit diversity (TD), receive diversity (RD), and angle diversity. For the TD, multiple transmitters with single receiver (MISO) is used to obtain the target location using frequency division multiplexing (FDM) and variable phase approach. To deploy RD, the proposed system comprises multiple receivers and single transmitter (SIMO). Only one transmitter's absolute coordinates are required to obtain the location coordinates of optical receivers. FDM and time division multiplexing access schemes have been utilized to transmit the unique signals by light‐emitting diodes (LEDs). To implement angle diversity, orientation of transmitter and receiver is considered for positioning purposes. Under this environment, there is no constraint for the LEDs to be installed on ceiling; they can be at any point within the room or at different heights. The information regarding spherical coordinates (representing the orientation) of LEDs (single or multiple) is used to determine the coordinates or the orientation of receiver having multiple or single photodiodes, respectively. Under multiple orientations, this system is analyzed with both scenarios, that is, MISO and SIMO.

Emotions detection in natural languages is very effective in analyzing the user's mood about a concerned product, news, topic, and so on. However, it is really a challenging task to extract important features from a burst of raw social text, as emotions are subjective with limited fuzzy boundaries. These subjective features can be conveyed in various perceptions and terminologies. In this article, we proposed an IoT-based framework for emotions classification of tweets using a hybrid approach of Term Frequency Inverse Document Frequency (TFIDF) and deep learning model. First, the raw tweets are filtered using the tokenization method for capturing useful features without noisy information. Second, the TFIDF statistical technique is applied to estimate the importance of features locally as well as globally. Third, the Adaptive Synthetic (ADASYN) class balancing technique is applied to solve the imbalance class issue among different classes of emotions. Finally, a deep learning model is designed to predict the emotions with dynamic epoch curves. The proposed methodology is analyzed on two different Twitter emotions datasets. The dynamic epoch curves are shown to show the behavior of test and train data points. It is proved that this methodology outperformed the popular state-of-the-art methods.

Multilingual and multimodal data analysis is the emerging news feed evaluation system. News feed analysis and evaluations are interrelated processes, which are useful in understanding the news factors. The news feed evaluation system can be implemented for single or multilingual language models. Classification techniques used on multilingual news analysis require deep layered learning techniques rather than conventional approaches. In this proposed work, a hierarchical structure of deep learning algorithms is implemented for making an effective complex news evaluation system. Deep learning techniques such as the Deep Cooperative Multilingual Reinforcement Learning Model, the Multidimensional Genetic Algorithm, and the Multilingual Generative Adversarial Network are developed to evaluate a vast number of news feeds. The proposed tech-niques collaborate in a pipeline order to build a deep news feed evaluation system. The implementation details project that the newly proposed system performs 5% to 12% better than the other news evaluation systems.

Technological developments in communication technologies in the form of hardware and software have made unilateral sensor connectivity over Internet access that facilitates data observation and measurement of physical entities. A technology known as Internet of Things (IoT) is commonly referred to as the connectivity of Internet devices that provides the communication interactivity between the physical and the cyber objects. One of the key objectives of Internet computing is to simplify human activities and improve the user experience and device access. To explore its basic challenges, big data is somehow diversified into smart-data intelligence that transforms the raw semantic data into smart-data. The transformation approaches realize the significance of productivity and financial gain, which in turn offers a better decision-making process and privacy preservation. Moreover, the intelligent system collects raw data from different devices that analyze the extracted information. Since IoT plays a significant role in the development of a new source dataset, a seamless authentication protocol (SAP) is preferably chosen to coalesce data inference, algorithm development, and technological advancement. The comparative analysis proves that the proposed SAP consumes less computation and communication overhead as compared to other authentication schemes.

The perceptual experience of next-generation wireless networks is nowadays coexisting with a unified heterogeneous system. It uses advanced internet protocol (IP) features with scalable infrastructure to optimize the efficiency rate of the core networks. Of late, the Internet-of-Things has played a significant role in the growth of smart devices that derive a forward and backward interface to provide a low-rate data flow. Moreover, a software-defined network (SDN) is openly chosen to explore the promising features such as controller and switches to separate the control and data plane. To fulfill the standard constraints of 6G networks, this article presents dynamic-driven congestion control and segment rerouting. It has two essential purposes: 1) to mitigate the flow rate and signal congestion and 2) to enhance the monitoring process and path adjustment. The approach of Deleroi superimposed, forward-backward interface, and segment rerouting have been implemented and configured in the IMSCore platform that examines the quality metrics such as throughput rate and transmission delay. To probe the signaling traffic and congestion rate, two superposition principles were derived that can minimize the transmission rate and signaling congestion to achieve the demands of the media zone.

Coronavirus Disease 19 (COVID-19) is a highly infectious viral disease affecting millions of people worldwide in 2020. Several studies have shown that COVID-19 results in a severe acute respiratory syndrome and may lead to death. In past research, a greater number of respiratory diseases has been caused by exposure to air pollution for long periods of time. This article investigates the spread of COVID-19 as a result of air pollution by applying linear regression in machine learning method based edge computing. The analysis in this investigation have been based on the death rates caused by COVID-19 as well as the region of death rates based on hazardous air pollution using data retrieved from the Copernicus Sentinel-5P satellite. The results obtained in the investigation prove that the mortality rate due to the spread of COVID-19 is 77% higher in areas with polluted air. This investigation also proves that COVID-19 severely affected 68% of the individuals who had been exposed to polluted air.

A mobile ad hoc network (MANET) is summarized as a combination device that can move, synchronize and converse without any preceding management. Enhancing the lifetime energy is based on the status of the concerned channel. The node is accomplished of control the control messages. Due to unplanned methods of energy conservation, the node lifespan and quality of packet flow is defaced in the existing solution. It results in a network-to-node-energy trade-off, ensuing in a failure of the post-network. This failure results in reduced time-to-live and higher overhead. This paper discusses an effective buffer management mechanism, in addition to proposing a novel performance modeling in Volunteered Computing MANET and tactile internet Next, the best execution the nodes can accomplish under fractional data is completely portrayed for utilities for a general purpose. To associate the space between network efficiency and energy conservation based on the minimal overhead, this article proposes a switch state promoting mutual Optimized MAC protocol for conservation of a node's energy and the optimal use of available nodes before their energy drain. Simulation results are provided as proof of the proposed solution. The simulation results are compared with the existing system with performance measures of delay, throughput, energy consumption, and availability of the node.

The advanced computational capabilities of many resource constrained devices, such as smartphones have enabled various research areas including image retrieval from big data repositories for numerous Internet of Things (IoT) applications. The major challenges for image retrieval using smartphones in an IoT environment are the computational complexity and storage. To deal with big data in IoT environment for image retrieval, this paper proposes a light-weighted deep learning-based system for energy-constrained devices. The system first detects and crops face regions from an image using Viola-Jones algorithm with additional face and nonface classifier to eliminate the miss-detection problem. Second, the system uses convolutional layers of a cost effective pretrained CNN model with defined features to represent faces. Next, features of the big data repository are indexed to achieve a faster matching process for real-time retrieval. Finally, Euclidean distance is used to find similarity between query and repository images. For experimental evaluation, we created a local facial images dataset, including both single and group facial images. This dataset can be used by other researchers as a benchmark for comparison with other real-time facial image retrieval systems. The experimental results show that our proposed system outperforms other state-of-the-art feature extraction methods in terms of efficiency and retrieval for IoT-assisted energy-constrained platforms.

Internet of Things (IoT) and cloud computing are steadily growing in support of recent and projected revolutionary Internet applications. Cloud computing has the ability to meet the performance expectations of different applications. In this paper, we present the implementation of applications based on cooperative resource and energy-constrained objects with optimized performances. To dynamically incorporate objects into IoT applications' execution, task scheduling should be implemented for resource allocation in an optimized manner. We propose a task scheduling algorithm based on robust canonical particle swarm optimization (CPSO) and fully-informed particle swarm (FIPS) algorithms in order to solve the problem of resource allocation and management in both homogeneous and heterogeneous IoT cloud computing. Our objective is to satisfy the Quality of Service (QoS) in terms of throughput and delay, by performing optimal task scheduling taking into consideration different classes of data traffic. Performance evaluation of experiments show that throughput and delay can be significantly improved by dynamic dedicated servers (DDSS) and heterogeneous DDSS (h-DDSS) using FIPS optimization algorithm compared to CPSO optimization algorithm.

This paper investigates the optimal number of antennas at a base station, in contrast to what has been accepted in the past: that increasing the number of antennas at base station always enhances performance. In this study, we show that increasing the number of antennas does not always improve the desired performance. Additionally, such increase in antennas consumes more power in transmission and adds to the computation complexity, which in turn needs more time and is more difficult to implement. The optimum number of antennas has been evaluated using simulations. The simulation results show that the optimal ratio equals to 1.2 times the number of active users in each time frame.

Industrial Internet of Things (IIoTs) is the fast growing network of interconnected things that collects and exchange data using embedded sensors planted everywhere. Several IIoT applications such as the ones related to healthcare systems are expected to widely utilize the evolving 5G technology. This 5G-inspired IIoT paradigm in healthcare applications enables the users to interact with various types of sensors via secure wireless medical sensor networks (WMSNs). Users of 5G networks should interact with each other in a seamless secure manner. And thus, security richness is highly coveted for the real time wireless sensor network systems. Asking users to verify themselves before every interaction is a tedious, time-consuming process that disrupts inhabitants' activities, and degrades the overall healthcare system performance. To avoid such problems, we propose a context-sensitive seamless identity provisioning (CSIP) framework for the IIoT. CSIP proposes a secure mutual authentication approach using hash and global assertion value to prove that the proposed mechanism can achieve the major security goals of the WMSN in a short time period.

Internet of Things (IoTs) refers to the rapidly growing network of connected objects that are able to collect and exchange data using embedded sensors. To guarantee the connectivity among these objects and devices, fault tolerant routing has been received a significant attention in recent years. In this paper, we propose a bio-inspired particle multi-swarm optimization (PMSO) routing algorithm to construct, recover and select k-disjoint paths that tolerates the failure while satisfying quality of service (QoS) parameters. Multi-swarm strategy enables determining the optimal directions in selecting the multipath routing while exchanging messages from all positions in the network. The validity of the proposed algorithm is assessed and results demonstrate high-quality solutions compared to the canonical particle swarm optimization (CPSO), and fully particle multiswarm optimization (FPMSO).

In this paper, we propose a new architecture to read the smart meters which are commonly distributed nowadays in smart cities. In this architecture, public transportation vehicles are utilized as Data Collectors (DCs) that reads these smart meters. Moreover, we target the path planning problem for these DCs given that a limited number of vehicles with a specific storage capacity are able to participate in collecting readings from these meters. We optimize the number of DCs while maintaining their minimum travelling distances and satisfied traffic constraints. We propose a Genetic-based Routing (GR) approach for more optimized solutions. Extensive simulation results are performed to confirm the effectiveness of the proposed approach in comparison to other heuristic approaches.

Contention-free Medium Access Control (MAC) protocols in Wireless Sensor Networks (WSNs) have higher energy efficiency and lower packet latency than contention-based ones due to reduced idling and allow efficient utilization of energy supplies of sensors. This paper presents evaluation the performance of Time Division Multiple Access (TDMA) duty-cycled MAC for multihop WSNs. We propose a semi-Markov chains by considering power consumption in different operational models to analyze the Quality of Services (QoS) parameters in terms of energy consumption, delay, and throughput. We show how the model can be applied to impose architectural decisions and compute energy duty-cycles.

In this paper we propose a hybrid heuristic approach for public data delivery under ultra-large-scale smart-city settings. In this approach, public transportation vehicles are going into election process to be utilized as Mobile Couriers (MCs) that read public Access Points (APs) data loads and relay it back to a central processing base-station. We also introduce a cost-based fitness function for the MCs election in the smart-city project which forms a real implementation for the Internet of Things (IoT) paradigm. Our cost-based function considers mobile resource limitations in terms count, storage and energy. Extensive simulations are performed and the results confirm the effectiveness of the proposed approach in comparison to other heuristic approaches with identical objectives.

In this paper, we envision future sensor networks to be operating as information-gathering networks in large-scale Internet-of-Things applications such as smart cities, which serve multiple users with diverse quality-of-information (QoI) requirements on the data delivered by the network. To learn data delivery paths that dynamically adapt to changing user requirements in this information-centric sensor network (ICSN) environment, we make use of cognitive nodes that implement both learning and reasoning in the network. In this paper, we focus on the learning strategies and propose two techniques, namely learning data delivery A* (LDDA*) and cumulative-heuristic accelerated learning (CHAL) that use heuristics to improve the success rate of data delivered to the sink in the cognitive ICSN. While LDDA* updates a single heuristic function to choose paths that can deliver data with good QoI to the sink, CHAL accumulates heuristic values from multiple observations from the environment to choose data delivery paths that are more resource aware and considerate toward the energy consumption of the network. Extensive simulations have shown improvement of about 40% in the average rate of successful data delivery to the sink with the use of heuristic learning, when compared with a network that did not implement any learning.

Small cell deployments have proven to be a cost-effective solution to meet the ever growing capacity and coverage requirements of mobile networks. While small cells are commonly deployed indoors, more recently outdoor roll-outs have garnered industry interest to complement existing macrocell infrastructure. However, the problem of where and when to deploy these small cells remains a challenge. In this paper, we investigate the small base station (SBS) placement problem in high demand outdoor environments. First, we propose a dynamic placement strategy (DPS) that optimizes SBS deployment for two different network objectives: minimizing data delivery cost, and minimizing macrocell utilization. We formulate each problem as a mixed integer linear program (MILP) that determines the optimal set of deployment locations among the candidate hot-spots to meet each network objective. Then we develop two greedy algorithms, one for each objective, that achieve close to optimal MILP performance. Our simulation results demonstrate that significant delivery cost and MBS utilization reductions are possible by incorporating the proposed deployment strategies.

Precision Agriculture (PA) is a challenging application for Wireless Sensor Networks (WSNs). The network has to deal with large deployment areas, multiple surface terrains with diverse requirements on information gathering such as energy consumption, and these operations need to be mostly unattended. Mobile robots (data collectors) when used in WSNs of such demanding applications enable them to handle the limited communication ranges of these tiny sensors and simultaneously cater to multiple end-user requests. In this work, we present Cognitive Path Planning (CPP) for mobile Data Collectors (DC) in WSNs to efficiently collect the sensed data in a PA application. Energy consumption is the major attribute that impacts the performance of the proposed approach, and hence, it is our target in this paper.

Recently, many natural disasters have occurred (e.g., the 2011 tsunami in Japan). In response to these disasters, Wireless Sensor Networks have been deployed to improve their detection level. This important technology has several significant challenges, subsequently, this paper focuses on the problem of routing. Especially, a new set of dynamic versions of Sensing Sphere close to the Line:Smallest Angle to the Line (SSL:SAL) (El Salti et al.) is proposed. These versions are the SSL:SAL version 1 and version 2 (SSL:SALv1 and SSL:SALv2, respectively). This paper also conducts some experiments where it demonstrates the following: 1) packet delivery is a control factor that impacts several metrics, 2) the two versions of SSL:SAL increase the ability to improve the packet delivery even though the regions are partially covered, 3) the SSL:SALv1 and SSL:SALv2 achieve short hop-based paths, and 4) the SSL:SALv1 achieves short Euclidean-based paths. The proposed protocols are compared to some existing position-based protocols. Moreover, the experiments show generally that trade-offs exist between these metrics.

Smart Cities are a challenging application for Wireless Sensor Networks (WSNs). The network has to deal with large deployment areas, multiple user requests with diverse requirements on information attributes such as latency and reliability, and these operations need to be mostly unattended. Elements of cognition when used in the network nodes of such demanding applications of WSNs enable them to handle the heterogeneous traffic flows and simultaneously cater to multiple end-user requests. In this work, we present Cognitive Information-Centric sensor networks (CICSN), a paradigm of WSNs in which sensory information is identified using attribute-value pairs, and elements of cognition are used to deliver data to the sink with user-desired quality of information. Latency and reliability are identified as attributes that impact the quality of information (QoI) perceived by the end user. With a use-case analysis, we show how the CICSN is able to provide user-desired QoI on the delivered data.

In integrated RFID and Wireless Sensor Networks (RSNs), the abundance of wirelessly enabled mobile devices facilitates forwarding data packets. This presents a beneficial alternative to offload transmission from relay nodes to access points. However, there is no incentive for such mobile devices to carry the relaying task. Hence, we introduce heuristics for Monetary-based Courier Relaying (MCR) that incorporates price negotiation for relaying from source nodes to access points via mobile couriers in RSN architectures. Our heuristics employ a threshold price for each packet prior to transmission. Whether to forward the packet to a courier or to directly transmit it to access points depends on a criticalness function, in addition to the courier's charge with respect to the packet's threshold price. We compare our MCR model with other dominant mobile Ad hoc delivery schemes; eliciting its efficiency in terms of energy, cost and delivery rate.

The ever growing data traffic generated by users in cellular networks is becoming more challenging and straining for cellular operators. Thus, developing efficient mechanisms that enable cellular operators to offload data traffic from their networks in a cost-effective manner is essential. To this end, we propose a generic framework (MFW) that exploits femtocells and WiFi networks. The framework allows cellular operators to offload part of the traffic load generated by mobile users in public transportation systems, viz.; buses, streetcars. Regular Femto Base Stations (FBSs) are installed in these vehicles to offer cellular coverage for mobile devices, called the mobile FBS (mobFBS). The mobFBS utilizes ubiquitous WiFi access points as a backhaul to route the traffic to the cellular operator's network through WiFi instead of the loaded macrocells. Mobile data users are categorized in our framework in different prioritized classes in order to efficiently allocate the mobFBS bandwidth to the maximum number of users. Efficiency is considered in terms of bandwidth utilization, enhancing capacity and managing grouped data traffic in vehicles. We elaborate on the performance of MFW via numerical experiments, emulating practical applications, viz. “Skype” and “YouTube”, and demonstrate the efficiency of our framework in terms of data traffic offloading.

Public sensing is an application in which sensory systems embedded in smart devices, vehicles, residential and public spaces form a collective cloud of data sources from which multi-owned access points realize end-users' service requests. This conception can be further extended under the umbrella of integrated RFID-Sensor Networks (RSNs) to include RFID systems. Such a configuration is heterogeneous by nature and faces many challenges in terms of data delivery and resource management. In this paper, we represent a Reciprocal Public Sensing (RPS) scheme for integrated RSN architectures. Our scheme incorporates heuristic solutions for static sensors and mobile data collectors, in addition to a reciprocal agreement for data exchange over the tiers of the proposed architecture adhering to the social welfare of the network as a whole. We provide simulation results showing how RPS outperforms other data delivery schemes in terms of minimizing delay, packet loss, and energy consumption, in addition to prolonging the overall network lifetime.

In this paper, we present an Enhanced 2-Phase Data Delivery (E2-PDD) framework for Information-Centric Networks (ICNs), focusing on efficient content access and distribution as opposed to mere communication between data consumers and publishers. We employ an approach of growing eminence, where requests are initiated by consumers seeking particular services that are data-dependent. High-level Controllers (HCs) receive the consumers' requests and issue queries to a multitude of data publishers. The publishers in our topology include a wide variety of ubiquitous nodes that could be either stationary or mobile, operating under different protocols. In order to consider fundamental challenges in ICNs such as node mobility and data disruption, our E2-PDD framework employs Low-level Controllers (LCs) that act as moderators between the HCs and the data publishers, executing data queries for a top tier and replying back with a set of candidate rendezvous points obtained from a bottom tier. The HCs maximize selection based on the nearest rendezvous. Extensive simulation results have been used to evaluate our E2-PDD framework in terms of key performance metrics in ICNs viz., average in-network delay, and publisher load, given different mobility pause time durations and data consumers' densities.

In this paper, we present URIA, a Ubiquitous Robust Integrated Approach for data delivery in integrated RFID-Sensor Networks (RSNs). The proposed approach deploys ubiquitous wireless nodes equipped with transceivers as couriers between integrated reader/relay nodes and access points in an IoT setting. In addition to guaranteeing a specific level of connectivity across the network, URIA maintains constraints on delay, such that a multi-path minimal-delay route is always provided between any source-destination pair. Our approach is formulated via a Semi-Definite Programming (SDP) solution and is compared against other IoT integrated schemes targeting connectivity and delay metrics. Simulation results show that our proposed approach outperforms rival schemes in terms of total latency and delivery rate. This is achieved while considering vast data generation rates, instantaneous topology changes, and high probabilities of failure over the established end-to-end paths.

Recent research endeavours are capitalizing on state of the art technologies to build a scalable Internet of Things (IoT). Envisioned as a technology to integrate the best of Wireless Sensor Networks and RFID systems, there is much promise for a global network of objects that are identifiable, track-able, and harmoniously informing. However, the realization of an IoT framework is hindered by many factors, the most pressing of which is attributed to the integration of these heterogeneous nodes and devices. A considerable subset of these nodes undergoes movement and dynamically enters and leaves the network backbone/topology. Routing packets and inter-nodal communication has received little attention; mainly due to the sheer reliance on the Internet as a backbone. However, spatially correlated entities in the IoT, and those which most often interact, would pose a significant overhead of communication if all intermediate packets need to be routed over distant backhauls. In remedy, we present a Pruned Adaptive IoT Routing (PAIR) protocol that selectively establishes routes of communication between IoT nodes. Since nodes in the IoT belong to different owners, we also introduce a pricing model to cater for the exchange of monetary costs by intermediate nodes to utilize their relaying resources. We also establish a cap on inter-nodal routing to dynamically utilize the Internet backbone if the source to destination distance surpasses a preset (case optimized) threshold. The PAIR routing protocol is elaborated upon, building upon the detailed system model presented in this paper. We finally present a use case to demonstrate the utility and practicality of PAIR in the heterogeneous IoT as it scales.

Advances in sensing and wireless communication technologies have enabled a wide spectrum of Outdoor Wireless Sensor Network (OWSN) applications. Some applications require the existence of a communication backbone federating different OWSN sectors, in order to collaborate in achieving more sophisticated missions. Federating (connecting) these sectors is an intricate task due to the huge distances between them, and due to the harsh operational conditions. A natural choice in this case is to have multiple Relay Nodes (RNs) that provide vast coverage and sustain the network connectivity in harsh environments. However, these RNs are not cheap and, thus, a constraint on their count holds. That being said and considering the harsh conditions in outdoor environments, placement of the RNs becomes crucial and has to be in a way that tolerates failures in communication links and deployed nodes. In this paper, we propose a novel approach in optimizing the RNs placement, called ST-DT approach, with the objective of federating different OWSNs with the maximum connectivity under a cost constraint on the RNs count to be deployed. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in large-scale outdoor environments.

Federating partitioned Wireless Sensor Networks (WSNs) in Outdoor Environment Monitoring (OEM), where the deployed sensor nodes are prone to significant damage and harsh operational conditions, becomes a necessity to prolong the WSN lifetime. Consequently, redundancy-based deployment strategies have been extensively studied in the literature. However, federating WSNs using node redundancy is expensive in OEM due to large-scale targeted areas, and frequent node/link failures. A natural choice in defeating these challenges is to employ multiple Data Collectors (DCs) that provide extendable and sustainable WSNs in harsh environments for long lifetime intervals. In this paper, we propose a grid-based deployment for DCs in which they are optimally repositioning on the grid vertices to connect disjointed WSN sectors. Towards this optimality, we design an Optimized DCs Repositioning (ODR) approach that maximizes the federated WSN lifetime while maintaining cost and connectivity constraints. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in outdoor environments.

Nowadays Wireless Sensor Networks (WSNs) are used to provide vast coverage areas in environmental applications, and thus relay nodes with wide transmission ranges are employed. However, these relays usually operate under harsh conditions with a very limited energy resources, making the network very prone to severe node failures and disconnectivities. In this paper, we propose a proactive Optimized Relay Repositioning (ORR) approach in which relays are regularly repositioned to maintain a specific level of fault-tolerance in addition to minimize the total network energy consumption. ORR is a grid-based approach, in which nodes are placed on grid vertices to limit the huge search space in large-scale environmental applications. This approach is formulated as a Mixed Integer Linear Program (MILP) for solid mathematical solutions. Extensive simulations and comparisons, assuming practical considerations of signal propagation and connectivity, show that our fault-tolerant approach can introduce a significant lifetime extension as compared to other heuristic and MILP-based approaches.

Federating Wireless Sensor Networks (WSNs) in Outdoor Environment Monitoring (OEM) becomes a necessity as advances in sensing technologies are achieved. Where several WSN sectors pursuing identical/different tasks intend to collaborate with each other in order to achieve more sophisticated and challenging missions, or intend to recover a significant damage in the network. Connecting (federating) these sectors is an intricate task due to the huge distances between the sectors, and the harsh operational conditions. A natural choice in defeating these challenges is to have multiple relay nodes that provide vast coverage areas and sustain the network connectivity in harsh environments. However, these relays are expensive and thus, the least number of such devices has to be populated. In this paper, we propose a grid-based deployment for relay nodes in which the relays are efficiently placed on the grid vertices to connect the disjointed WSN sectors. Towards this efficiency, we design an Optimized Relay Placement (ORP) approach that maximizes the disjointed sectors connectivity while maintaining cost constraints. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in outdoor environments.

ABSTRACT Advances in sensing and wireless communication technologies have enabled a wide spectrum of Outdoor Environment Monitoring applications. In such applications, several wireless sensor network sectors tend to collaborate to achieve more sophisticated missions that require the existence of a communication backbone connecting (federating) different sectors. Federating these sectors is an intricate task because of the huge distances between them and because of the harsh operational conditions. A natural choice in defeating these challenges is to have multiple relay nodes (RNs) that provide vast coverage and sustain the network connectivity in harsh environments. However, these RNs are expensive; thus, the least possible number of such devices should be deployed. Furthermore, because of the harsh operational conditions in Outdoor Environment Monitoring applications, fault tolerance becomes crucial, which imposes further challenges; RNs should be deployed in such a way that tolerates failures in some links or nodes. In this paper, we propose two optimized relay placement strategies with the objective of federating disjoint wireless sensor network sectors with the maximum connectivity under a cost constraint on the total number of RNs to be deployed. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in outdoor environments. Copyright © 2011 John Wiley & Sons, Ltd.

Grid-based deployments of Wireless Sensor Networks (WSNs) are widely used in a multiplicity of applications. However, practical factors such as communication irregularity and placement uncertainty have to be considered for more efficient deployments. In this paper, we examine connectivity properties of the 3D grid-based deployment when sensor placements are subject to random errors around their corresponding grid locations and hindrances to wireless communication channels exist. A generic approach is proposed to evaluate the average connectivity of the deployed network. This generic approach is independent of the grid-shape, random error distributions, and the environment wireless channel characteristics. The average connectivity is computed numerically and verified via extensive simulations. Based on the numerical results, quantified effects of positioning errors and grid edge length on the average connectivity are demonstrated. Furthermore, we discuss several ways of achieving efficient grid-based deployment planning for connectivity, and illustrate these approaches through numerical examples.

In this paper, we propose two schemes for sensor and relay node placement in environmental sensing applications. The first scheme aims at maximizing the network lifetime by reducing the total energy consumption. The second does so while maintaining fault-tolerance constraints. It guarantees a lower bound on the minimum required number of faulty nodes. Both schemes are based on a 3-D hierarchical architecture, in which nodes are placed on grid vertices to limit the search space. We divide the lifetime of the network into fixed-length rounds and find the placement which reserves more energy in each round to prolong the lifetime. These problems are formulated via Integer Linear Programs (ILPs). An ILP solver is used to find the optimal placement of nodes in addition to multi-hop routing from the sensors to the base-station in both schemes. Extensive simulations and comparisons, assuming practical considerations of signal propagation and connectivity, show that our fault-tolerant scheme introduces a significant lifetime extension as compared to the first one under the same harsh operational conditions.

Device deployment plays a key role in the performance of any large-scale wireless sensor network (WSN) application. WSN device deployment (i.e. the numbers and positions of the devices) must consider several design factors, viz. coverage, connectivity, lifetime, etc. However, connectivity remains the most fundamental factor especially in a large scale harsh environment. In this paper, we explore the problem of relay node (RN) placement in 3D forestry space. We formulate a generalized RN deployment optimization problem aimed at maximizing the network connectivity with constraints on RNs count. We investigate how the number of RNs can affect the connectivity of a WSN in a harsh environment. Based on quantitative analysis of such effects, the paper sets a threshold on the minimum number of required RNs.

Maximizing network connectivity while maintaining a useful period of lifetime is a challenging design objective for wireless sensor networks (WSNs). Satisfying such objective becomes an even more intricate task in harsh operational environments such as those found in forestry applications. While much work has been presented aimed at forestry applications, only a few have addressed the unique characteristics of forestry settings, such as 3-D deployment and operational requirements. In this paper, we introduce a novel deployment strategy for relay nodes in WSNs for forestry applications. The strategy optimizes network connectivity, while guarantying specific network lifetime. Key to our contribution is a revised definition for network lifetime that is more realistic and more fitting to forestry applications. The effectiveness of our strategy is validated through extensive simulation and comparisons.

Diagnostic march tests are powerful tests that are capable of detecting and identifying faults in memories. Although march SS was published for detecting simple static faults, no test has been published for identifying all faults possibly present in memory cells. In this paper, we target all published simple static faults. We identify faults that cannot be distinguished due to their analog behavior. We present a new methodology for generating irredundant diagnostic march tests for any desired subset of the simple static faults using the necessary and sufficient conditions for fault detection. Using that methodology, along with a verification tool, and trial and error, we were able to build a new diagnostic test for all distinguishable faults named march DSS. March DSS is the first test that is capable of identifying all distinguishable memory static faults. Compared to the latest most comprehensive published diagnostic march test, march DSS provides significant improvement in terms of fault coverage, time complexity, and power consumption. By targeting the same faults, we were able to provide a new test equivalent to the latest published test with 46% improvement in time complexity.